

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Tuple             =       KandinskyVaaInpaintPipeline
							UpperCAmelCase__			:		List[str]             =       ["image_embeds", "negative_image_embeds", "image", "mask_image"]
							UpperCAmelCase__			:		List[str]             =       [
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							UpperCAmelCase__			:		int             =       [
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase__			:		Dict             =       False


							@property
							def 							__lowercase							(     self			)       ->       List[Any]:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return self.time_input_dim


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return self.time_input_dim * 4


							@property
							def 							__lowercase							(     self			)       ->       List[Any]:
												return 1_0_0


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												torch.manual_seed(0			)

												_a      :       Optional[int]   =						{
												    '''in_channels''': 9,
												    # Out channels is double in channels because predicts mean and variance
												    '''out_channels''': 8,
												    '''addition_embed_type''': '''image''',
												    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
												    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
												    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
												    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    '''layers_per_block''': 1,
												    '''encoder_hid_dim''': self.text_embedder_hidden_size,
												    '''encoder_hid_dim_type''': '''image_proj''',
												    '''cross_attention_dim''': self.cross_attention_dim,
												    '''attention_head_dim''': 4,
												    '''resnet_time_scale_shift''': '''scale_shift''',
												    '''class_embed_type''': None,
												}

												_a      :       Optional[Any]   =						UNetaDConditionModel(**_a			)
												return model


							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												return {
												    "block_out_channels": [3_2, 6_4],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 1_2,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												torch.manual_seed(0			)
												_a      :       Optional[Any]   =						VQModel(**self.dummy_movq_kwargs			)
												return model


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       List[str]   =						self.dummy_unet
												_a      :       List[Any]   =						self.dummy_movq

												_a      :       Union[str, Any]   =						DDIMScheduler(
												    num_train_timesteps=1_0_0_0			,				beta_schedule='''linear'''			,				beta_start=0.0_0085			,				beta_end=0.012			,				clip_sample=_a			,				set_alpha_to_one=_a			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=_a			,				)

												_a      :       int   =						{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components


							def 							__lowercase							(     self			,				_a			,				_a=0			)       ->       str:
												_a      :       List[str]   =						floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       int   =						floats_tensor((1, self.text_embedder_hidden_size)			,				rng=random.Random(seed + 1			)			).to(
												    _a			)
												# create init_image
												_a      :       Union[str, Any]   =						floats_tensor((1, 3, 6_4, 6_4)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       Union[str, Any]   =						image.cpu().permute(0			,				2			,				3			,				1			)[0]
												_a      :       Any   =						Image.fromarray(np.uinta(_a			)			).convert('''RGB'''			).resize((2_5_6, 2_5_6)			)
												# create mask
												_a      :       Optional[int]   =						np.ones((6_4, 6_4)			,				dtype=np.floataa			)
												_a      :       Dict   =						0

												if str(_a			).startswith('''mps'''			):
																	_a      :       List[str]   =						torch.manual_seed(_a			)
												else:
																	_a      :       Optional[int]   =						torch.Generator(device=_a			).manual_seed(_a			)
												_a      :       List[str]   =						{
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       str   =						'''cpu'''

												_a      :       Union[str, Any]   =						self.get_dummy_components()

												_a      :       Optional[int]   =						self.pipeline_class(**_a			)
												_a      :       Optional[Any]   =						pipe.to(_a			)

												pipe.set_progress_bar_config(disable=_a			)

												_a      :       Optional[Any]   =						pipe(**self.get_dummy_inputs(_a			)			)
												_a      :       Optional[Any]   =						output.images

												_a      :       Any   =						pipe(
												    **self.get_dummy_inputs(_a			)			,				return_dict=_a			,				)[0]

												_a      :       Dict   =						image[0, -3:, -3:, -1]
												_a      :       Optional[int]   =						image_from_tuple[0, -3:, -3:, -1]

												print(F"""image.shape {image.shape}"""			)

												assert image.shape == (1, 6_4, 6_4, 3)

												_a      :       int   =						np.array(
												    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]			)

												assert (
												    np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
												), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
												), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



@slow
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       List[str]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       List[str]   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''			)

												_a      :       Tuple   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''			)
												_a      :       Dict   =						np.ones((7_6_8, 7_6_8)			,				dtype=np.floataa			)
												_a      :       int   =						0

												_a      :       Dict   =						'''a hat'''

												_a      :       Dict   =						KandinskyVaaPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-2-prior'''			,				torch_dtype=torch.floataa			)
												pipe_prior.to(_a			)

												_a      :       Union[str, Any]   =						KandinskyVaaInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''			,				torch_dtype=torch.floataa			)
												_a      :       Union[str, Any]   =						pipeline.to(_a			)
												pipeline.set_progress_bar_config(disable=_a			)

												_a      :       str   =						torch.Generator(device='''cpu'''			).manual_seed(0			)
												_a ,   _a      :       Union[str, Any]   =						pipe_prior(
												    _a			,				generator=_a			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

												_a      :       Union[str, Any]   =						pipeline(
												    image=_a			,				mask_image=_a			,				image_embeds=_a			,				negative_image_embeds=_a			,				generator=_a			,				num_inference_steps=1_0_0			,				height=7_6_8			,				width=7_6_8			,				output_type='''np'''			,				)

												_a      :       Tuple   =						output.images[0]

												assert image.shape == (7_6_8, 7_6_8, 3)

												assert_mean_pixel_difference(_a			,				_a			)




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Optional[Any]         =    logging.get_logger(__name__)

__a    :	Optional[Any]         =    torch.device("""cpu""")





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]				)

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]				)

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]				)

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				dct.pop(lowercase				)
				__lowercase					     =				val





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				for k in state_dict.keys():
								__lowercase					     =				k
								if ".pwconv" in k:
												__lowercase					     =				k_new.replace('''.pwconv'''				,     '''.point_wise_conv'''				)
								if ".dwconv" in k:
												__lowercase					     =				k_new.replace('''.dwconv'''				,     '''.depth_wise_conv'''				)
								if ".Proj." in k:
												__lowercase					     =				k_new.replace('''.Proj.'''				,     '''.proj.'''				)
								if "patch_embed" in k_new:
												__lowercase					     =				k_new.replace('''patch_embed'''				,     '''swiftformer.patch_embed.patch_embedding'''				)
								if "network" in k_new:
												__lowercase					     =				k_new.split('''.'''				)
												if ls[2].isdigit():
																__lowercase					     =				'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]				)
												else:
																__lowercase					     =				k_new.replace('''network'''				,     '''swiftformer.encoder.network'''				)
								rename_keys.append((k, k_new)				)
				return rename_keys





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				__lowercase					     =				1000
				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				'''imagenet-1k-id2label.json'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				# size of the architecture
				if swiftformer_name == "swiftformer_xs":
								__lowercase					     =				[3, 3, 6, 4]
								__lowercase					     =				[48, 56, 112, 220]

				elif swiftformer_name == "swiftformer_s":
								__lowercase					     =				[3, 3, 9, 6]
								__lowercase					     =				[48, 64, 168, 224]

				elif swiftformer_name == "swiftformer_l1":
								__lowercase					     =				[4, 3, 10, 5]
								__lowercase					     =				[48, 96, 192, 384]

				elif swiftformer_name == "swiftformer_l3":
								__lowercase					     =				[4, 4, 12, 6]
								__lowercase					     =				[64, 128, 320, 512]

				# load state_dict of original model, remove and rename some keys
				if original_ckpt:
								if original_ckpt.startswith('''https'''				):
												__lowercase					     =				torch.hub.load_state_dict_from_url(lowercase				,     map_location='''cpu'''				,     check_hash=lowercase				)
								else:
												__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				__lowercase					     =				checkpoint

				__lowercase					     =				create_rename_keys(lowercase				)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowercase				,     lowercase				,     lowercase				)

				# load HuggingFace model
				__lowercase					     =				SwiftFormerForImageClassification(lowercase				).eval()
				hf_model.load_state_dict(lowercase				)

				# prepare test inputs
				__lowercase					     =				prepare_img()
				__lowercase					     =				ViTImageProcessor.from_pretrained('''preprocessor_config'''				)
				__lowercase					     =				processor(images=lowercase				,     return_tensors='''pt'''				)

				# compare outputs from both models
				__lowercase					     =				get_expected_output(lowercase				)
				__lowercase					     =				hf_model(inputs['''pixel_values''']				).logits

				assert hf_logits.shape == torch.Size([1, 1000]				)
				assert torch.allclose(hf_logits[0, 0:5]				,     lowercase				,     atol=1E-3				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"				)
				hf_model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--swiftformer_name""",
		    default="""swiftformer_xs""",
		    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
		    type=str,
		    help="""Name of the SwiftFormer model you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default="""./converted_outputs/""",
		    type=str,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

		__a    :	Optional[int]         =    parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)